By the end of this tutorial, you will be able to:
Landsat satellites have been continuously monitoring Earth’s surface since 1972, providing invaluable data for environmental monitoring, land use planning, and change detection. Landsat 8, launched in 2013, captures data across 11 spectral bands at 30-meter resolution.
In this tutorial, you’ll work with Landsat 8 imagery of the Aarhus
region in Denmark, learning how to process and visualize satellite data
using R’s raster package.
Note: If you encounter errors with
plotRGB() and the scale parameter, ensure your
raster package is up to date (update.packages("raster")).
Some older versions may have issues with large scale values.
Question 1: What package provides the
sp (spatial) classes that raster depends on?
(Hint: check the startup message)
The raster package provides three different structures
depending on your data:
| Function | Use Case |
|---|---|
raster() |
One file, one band |
brick() |
One file, multiple bands (e.g., RGB images) |
stack() |
Multiple files, multiple bands (e.g., Landsat GeoTIFFs) |
For Landsat data, we typically use stack() because each
spectral band is stored in a separate file.
Landsat 8 visible light bands: - Band 2: Blue (0.45-0.51 μm) - Band 3: Green (0.53-0.59 μm) - Band 4: Red (0.64-0.67 μm)
# Load the Blue Band (Band 2)
blue <- raster("./Data/LC08_L1TP_196021_20190629_20200827_02_T1_B2.TIF")
# Load the Green Band (Band 3)
green <- raster("./Data/LC08_L1TP_196021_20190629_20200827_02_T1_B3.TIF")
# Load the Red Band (Band 4)
red <- raster("./Data/LC08_L1TP_196021_20190629_20200827_02_T1_B4.TIF")
print(blue)## class : RasterLayer
## dimensions : 378, 421, 159138 (nrow, ncol, ncell)
## resolution : 30, 30 (x, y)
## extent : 567615, 580245, 6217755, 6229095 (xmin, xmax, ymin, ymax)
## crs : +proj=utm +zone=32 +datum=WGS84 +units=m +no_defs
## source : LC08_L1TP_196021_20190629_20200827_02_T1_B2.TIF
## names : layer
## values : 5665, 32311 (min, max)
Question 2: Use the print() function on
one of your raster objects. What is the resolution (pixel size) of
Landsat 8 data?
Landsat 8 Level 1 data uses 16-bit values ranging from 0 to 65535. We need to specify this range when plotting.
Note: If you get an error about alpha levels, try
using stretch = "lin" instead of the scale parameter.
# Plot the RGB composite
plotRGB(rgb.Scene,
r = 1, # Index of the Red channel in the RasterStack
g = 2, # Index of the Green channel in the RasterStack
b = 3, # Index of the Blue channel in the RasterStack
scale = 65535, # Maximum value for 16-bit data
axes = TRUE,
main = "Landsat Scene - Full Extent"
)Question 3: Why does the image appear very dark?
Landsat scenes cover over 30,000 km², which can make it difficult to see local features. Let’s crop to the Aarhus region.
# Define a raster with the boundary coordinates
# Coordinates are in latitude/longitude (WGS84)
boundary <- raster(
ymx = 56.20, # Maximum y coordinate (top border)
xmn = 10.09, # Minimum x coordinate (left border)
ymn = 56.10, # Minimum y coordinate (bottom border)
xmx = 10.29 # Maximum x coordinate (right border)
)The boundary needs to match the coordinate system of the Landsat data.
# Crop the Landsat image to the Aarhus region
AarhusReg <- crop(
x = rgb.Scene, # The raster to crop
y = boundary # The extent/boundary to crop to
)
# Plot the cropped RGB composite
plotRGB(AarhusReg,
r = 1, g = 2, b = 3,
scale = 65535,
axes = TRUE,
main = "Landsat - Aarhus Region"
)Note that the displayed image looks the same, since we already cropped the image in advance, so that you can load it on PositCloud. The original image was too large to load into memory.
Question 4: Does the cropped image still appear dark? Why?
When pixel values occupy only a small portion of the possible value range (0-65535), images appear dull: either very dark, bright or gray, lacking detail in color space. Contrast stretching redistributes values across the full display range. In an 8-bit image, this range is 0-255 and the ‘plotRGB()’ function assumes 255 as the maximum value by default. If ‘scale’ is defined, it is used as the new maximum value. If ‘stretch’ is defined, it will rescale the values ignoring ‘scale’ or the default maximum value of 255.
Linear stretching rescales values evenly from minimum to maximum.
# Apply linear stretching
plotRGB(AarhusReg,
r = 1, g = 2, b = 3,
scale = 65535,
stretch = "lin", # Type of stretch
axes = TRUE,
main = "Landsat - Aarhus\nLinear Stretch"
)Histogram stretching distributes values more evenly across the range based on their frequency. It applies a histogram equalization, which means that it stretches dense parts of the histogram and condenses sparse parts.
# Apply histogram stretching
plotRGB(AarhusReg,
r = 1, g = 2, b = 3,
scale = 65535,
stretch = "hist", # Type of stretch
axes = TRUE,
main = "Landsat - Aarhus\nHistogram Stretch"
)Question 5: Compare the two stretching methods. Which produces better results for this scene?
The linear stretch method is clearly better, indicating that our data may be somewhat equally distributed across their values.
But - the histogram amplifies the very dark values, revealing some other patterns.
For more control, we can manually define the stretch based on data percentiles.
Use the histogram function in R to explore data distributions.
Derive percentiles of the data distributions at 0%, 1%, 5%, 25%, 50%, 75%, 95%, 99% and 100% of the data. For example, the 75th percentile means that 75% of data points lie below this value.
# Calculate percentiles per band, for all bands
qs_ls <- quantile(AarhusReg, c(0,.01,.05,.25,.50,.75,.95,.99,1))
# Extract and store the extreme values (low and high) across bands for key percentiles:
min0 <- min(qs_ls[, "0%"])
min5 <- min(qs_ls[, "5%"])
max95 <- max(qs_ls[, "95%"])
max100 <- max(qs_ls[, "100%"])# Plot with custom stretch based on the 5th and 95th percentiles of the data distribution
plotRGB(
AarhusReg - min5, # Subtract minimum to shift range
scale = max95 - min5, # Set scale to 90th percentile range
zlim = c(0, max95 - min5), # Limit display range
axes = TRUE,
main = "Landsat - Aarhus\nCustom Stretch"
)Question 6: Why do we subtract min5 or
min0 from the raster before plotting?
Extra Question (advanced): What is a key difference between the custom stretch and the built-in image stretch option?
In the custom stretch, we define ourselves how we want to stretch the data, what data to omit - in the built-in option, the stretch is based on pre-defined cutoffs/methods.
The custom stretch takes the relationship between the distributions of the 3 bands into account, keeping the differences between the bands - thats why we mostly remove red values (image becomes more blue) when we subtract the min5 percentile. The built-in options treat the 3 bands the same way and overlays them on top of each other - aligning the distributions (mimicking a sort of atmospheric correction)
Gamma correction applies non-linear transformations to the data to remove data skewness, and therefore to ensure a more perceptually even representation of colors. The goal is to transform the data so that it is unskewed and closer to a normal distribution.
# Extract individual bands
blue_band <- AarhusReg[[3]]
green_band <- AarhusReg[[2]]
red_band <- AarhusReg[[1]]
# Rescale values to 0-1 range based on the 5th and 95th percentile for each band
max_range <- qs_ls[, "95%"]-qs_ls[, "5%"]
blue_band <- ((blue_band-qs_ls[3, "5%"]) / max_range[3])
green_band <- (green_band-qs_ls[2, "5%"]) / max_range[2]
red_band <- (red_band-qs_ls[1, "5%"]) / max_range[1]
# Set negative values to zero
blue_band[blue_band < 0] <- 0
green_band[green_band < 0] <- 0
red_band[red_band < 0] <- 0
# Set values above 1 to 1
blue_band[blue_band > 1] <- 1
green_band[green_band > 1] <- 1
red_band[red_band > 1] <- 1
# Plot histograms before correction
hist(blue_band) # Blue band# Apply gamma transformation: use exponent 0.5 for a square-root transformation
blue.Gamma <- blue_band^0.5 # Emphasize mid-tones with square-root transformation
green.Gamma <- green_band^0.5 # Emphasize mid-tones with square-root transformation
red.Gamma <- red_band^0.5 # Emphasize mid-tones with square-root transformation
# Plot histograms before correction
hist(blue.Gamma) # Blue band# Stack the gamma-corrected bands
rgb.Gamma <- stack(red.Gamma, green.Gamma, blue.Gamma)
# Plot
plotRGB(rgb.Gamma,
scale = 1,
zlim = c(0, 1),
axes = TRUE,
main = "Landsat - Aarhus\nGamma Correction"
)Note that there are many different ways to transform your data based on the original data distribution and the assumptions for your statistical analysis and data visualization. For example, you can look into log-transformation or the box-cox transformation that transforms data into a shape close to a normal distribution.
Landsat provides pre-processed “Natural Color” GeoTIFF files that require less work. They are meant for visualization purposes only and for data exploration, providing an image data stretch that is close to our human perception.
# Load the Natural Color Image (all bands in one file)
rgb.Nat.Color <- brick("./Data/LC08_L1TP_196021_20190629_20200827_02_T1_refl.TIF")
# Crop to Aarhus region
rgb.Nat.Color <- crop(
x = rgb.Nat.Color,
y = boundary
)
# Plot
plotRGB(rgb.Nat.Color,
axes = TRUE,
main = "Landsat - Aarhus\nNatural Color Image"
)Question 7: How does this compare to your manually created RGB composites?
Beyond so-called “true-color” RGB images, different band combinations using different wavelengths highlight different surface features. Below you will explore a few common false-color composites. Landsat bands are provided in brackets, e.g. (2) for band 2.
A very common false-color composite uses the near-infrared band assigned to the red color, the red band as green and the green band as blue (Landsat band numbers in brackets):
Bands: NIR (5), Red (4), Green (3)
Purpose: Vegetation health analysis
Healthy vegetation appears bright red because it strongly reflects near-infrared radiation. You can also find more information about Landsat bands and common false-color composites here.
# Load and crop near-infrared band
nir <- raster("./Data/LC08_L1TP_196021_20190629_20200827_02_T1_B5.TIF")
nir <- crop(x = nir, y = boundary)
# Create Color Infrared stack
rgb.ColInf <- stack(
nir, # NIR as red
AarhusReg[[1]], # Red as green
AarhusReg[[2]] # Green as blue
)
# Plot
plotRGB(rgb.ColInf,
r = 1, g = 2, b = 3,
scale = 65535,
stretch = "lin",
axes = TRUE,
main = "Landsat - Aarhus\nColor Infrared"
)Bands: SWIR-2 (7), SWIR-1 (6), Red (4)
Purpose: Vegetation and soil analysis
# Load and crop SWIR bands
swir1 <- raster("./Data/LC08_L1TP_196021_20190629_20200827_02_T1_B6.TIF")
swir1 <- crop(x = swir1, y = boundary)
swir2 <- raster("./Data/LC08_L1TP_196021_20190629_20200827_02_T1_B7.TIF")
swir2 <- crop(x = swir2, y = boundary)
# Create SWIR stack
rgb.SwInf <- stack(swir2, swir1, AarhusReg[[1]])
# Plot
plotRGB(rgb.SwInf,
r = 1, g = 2, b = 3,
scale = 65535,
stretch = "lin",
axes = TRUE,
main = "Landsat - Aarhus\nShort-Wave Infrared"
)Bands: SWIR-1 (6), NIR (5), Blue (2)
Purpose: Crop monitoring
# Create Agriculture stack
rgb.Agro <- stack(
swir1, # SWIR-1 as red
nir, # NIR as green
AarhusReg[[3]] # Blue as blue
)
# Plot
plotRGB(rgb.Agro,
r = 1, g = 2, b = 3,
scale = 65535,
stretch = "lin",
axes = TRUE,
main = "Landsat - Aarhus\nAgriculture"
)Question 8: In the Agriculture composite, what color indicates healthy vegetation?
The Normalized Difference Vegetation Index (NDVI) is a proxy for vegetation greenness and health:
\[NDVI = \frac{NIR - Red}{NIR + Red}\]
Important: Use Level 2 Surface Reflectance data (L2SP), not raw Level 1 data, for accurate indices.
# Load surface reflectance bands (Level 2)
red_sr <- raster("./Data/LC08_L2SP_196021_20190629_20200827_02_T1_SR_B4.TIF")
nir_sr <- raster("./Data/LC08_L2SP_196021_20190629_20200827_02_T1_SR_B5.TIF")
# Crop to area of interest
red_sr <- crop(x = red_sr, y = boundary)
nir_sr <- crop(x = nir_sr, y = boundary)
# Calculate NDVI using raster algebra
NDVI <- (nir_sr - red_sr) / (nir_sr + red_sr)
# Set negative values to NA (water, clouds)
NDVI[NDVI < 0] <- NA
# load viridis color palettes
install.packages("viridis")
library(viridis)
# Define color palettes
colors_vir <- viridis(256)
colors_divergent <- colorRampPalette(c("brown4", "white", "darkcyan"))(255) # used later for difference image
# Plot NDVI
plot(NDVI,
zlim = c(0, 0.6),
col = colors_vir,
colNA = "black",
main = "NDVI - Aarhus (2019)"
)Question 9: What do the different colors represent in the NDVI map?
Let’s compare NDVI between 1989 (Landsat 5) and 2019 (Landsat 8). What do you expect to see?
# Load 1989 Landsat 5 bands (note different band numbers!)
# Landsat 5: Band 3 = Red, Band 4 = NIR
red_sr.1989 <- raster("./Data/LT05_L2SP_196021_19890626_20200916_02_T1_SR_B3.TIF")
nir_sr.1989 <- raster("./Data/LT05_L2SP_196021_19890626_20200916_02_T1_SR_B4.TIF")
# Crop to boundary
red_sr.1989 <- crop(red_sr.1989, boundary)
nir_sr.1989 <- crop(nir_sr.1989, boundary)# Calculate NDVI difference
NDVI.difference <- NDVI - NDVI.1989
# Plot 2019 NDVI
plot(NDVI,
zlim = c(0, 0.6),
col = colors_vir,
colNA = "black",
main = "NDVI 2019"
)# Plot 1989 NDVI
plot(NDVI.1989,
zlim = c(0, 0.6),
col = colors_vir,
colNA = "black",
main = "NDVI 1989"
)# Plot difference
plot(NDVI.difference,
zlim = c(-0.4, 0.4),
col = colors_divergent,
colNA = "black",
main = "NDVI Difference\n[2019 - 1989]"
)Question 10: What patterns do you observe in the NDVI difference map? What might explain these changes?
Generally small increases in NDVI across urban areas, with some well-defined areas having very high increases in NDVI - these are probably some recently naturalized areas.
High decreases in larger well-defined areas outside most of urban development, probably agricultural expansion into natural areas or urban expansion into agriculture/natural areas.